Gap volume prediction and uncertainty analysis for aircraft wing assembly

Abstract

Gap measurement is necessary in aircraft assembly to size the shims and ensure product integrity.

However, the current industrial practice employs a manual approach which is not accurate and is rather time-consuming because of the trial-and-error processes involved. The measurement and shimming processes take nearly 30% of the cycle time during the wing assembly. Research shows that laser scanning-based measurement has the potential to accelerate the cumbersome process (for solid shims, the process time can be reduced by 97% [1]). However, there is still a lack of effective solutions for digital gap measurement with high accuracy.

Starting by focusing on rigid assembly, a new three-stage digital gap measurement method is proposed by using a laser scanner and data processing software. Extensive error analyses for the proposed method have been conducted to examine the effects of key error sources. Two new calibration methods have been proposed for the first time to rectify the measurement results based on newly formulated error propagation models. Experiments have been conducted to validate the digital gap measurement method as well as these calibration methods.

The digital gap measurement method is then extended to non-rigid assembly in which part deformation is considered. A semi-analytical approach has been proposed to measure the part thickness in order to measure gap sizes. Key error sources have been analyzed. Two new calibration methods have been proposed to improve the accuracy of results, followed by experimental validation. Results have shown that after calibration, the percent error has reduced by 25.776% on average. Moreover, the data collection and processing for experiments could be completed within five hours. Compared with the time cost of current gap measurement and shimming method in the aircraft industry (75 hours [2]), it shows that the digital method is more efficient since it avoids point-by-point measurement by manual tools.

Date of Award	Jul 2024
Original language	English
Awarding Institution	Queen's University Belfast
Sponsors	Engineering and Physical Sciences Research Council & China Scholarship Council
Supervisor	Yan Jin (Supervisor), Gasser Abdelal (Supervisor) & Mark Price (Supervisor)